library(tidyverse)
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library(ggplot2)
library(plotly)
## Warning: package 'plotly' was built under R version 4.2.2
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## Attaching package: 'plotly'
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## last_plot
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## filter
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## layout
energy = read_csv("archive/organised_Gen.csv")
## New names:
## Rows: 496774 Columns: 7
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): STATE, TYPE OF PRODUCER, ENERGY SOURCE dbl (4): ...1, YEAR, MONTH,
## GENERATION (Megawatthours)
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
colnames(energy) = c("ID", "year", "month", "state", "producer", "source", "generation")
energy = energy %>% select(-`ID`)
energy$Date<-as.Date(with(energy,paste(year,month,Day = 1,sep="-")),"%Y-%m-%d")
energy
## # A tibble: 496,774 × 7
## year month state producer source gener…¹ Date
## <dbl> <dbl> <chr> <chr> <chr> <dbl> <date>
## 1 2001 1 AK Total Electric Power Industry Coal 46903 2001-01-01
## 2 2001 1 AK Total Electric Power Industry Petro… 71085 2001-01-01
## 3 2001 1 AK Total Electric Power Industry Natur… 367521 2001-01-01
## 4 2001 1 AK Total Electric Power Industry Hydro… 104549 2001-01-01
## 5 2001 1 AK Total Electric Power Industry Wind 87 2001-01-01
## 6 2001 1 AK Total Electric Power Industry Total 590145 2001-01-01
## 7 2001 1 AK Electric Generators, Electric Ut… Coal 18410 2001-01-01
## 8 2001 1 AK Electric Generators, Electric Ut… Petro… 64883 2001-01-01
## 9 2001 1 AK Electric Generators, Electric Ut… Natur… 305277 2001-01-01
## 10 2001 1 AK Electric Generators, Electric Ut… Hydro… 104549 2001-01-01
## # … with 496,764 more rows, and abbreviated variable name ¹​generation
energy %>%
filter(producer == "Total Electric Power Industry", state == "US-TOTAL") %>%
ggplot() +
geom_point(aes(x = year, y = generation, color = source)) +
geom_smooth(aes(x = year, y = generation, color = source)) +
labs(title = "US total power generation per year")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>%
filter(producer == "Total Electric Power Industry", state == "TX") %>%
ggplot() +
geom_point(aes(x = year, y = generation, color = source)) +
geom_smooth(aes(x = year, y = generation, color = source)) +
labs(title = "Texas total power generation per year")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>%
filter(producer == "Total Electric Power Industry", state != "US-TOTAL") %>%
ggplot() +
geom_point(aes(x = state, y = generation, color = source))
energy %>%
filter(producer == "Total Electric Power Industry", state != "US-TOTAL", generation < -5000) %>%
ggplot() +
geom_point(aes(x = state, y = generation, color = source)) +
labs(title = "States with negative power generation (<-5000)")
energy %>%
filter(producer == "Total Electric Power Industry", state == "TX") %>%
ggplot() +
geom_point(aes(x = month, y = generation, color = source)) +
geom_smooth(aes(x = month, y = generation, color = source)) +
labs(title = "Texas total power generation per month")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>%
ggplot() +
geom_col(aes(x = year, y = generation, fill = source)) +
facet_wrap(vars(source))
US goal is to install an average of 30 GW of solar capacity per year between now and 2025 and 60 GW per year from 2025-2030. https://www.renewable-ei.org/pdfdownload/activities/01_Key_AlejandroMoreno.pdf
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", source == "Solar Thermal and Photovoltaic") %>%
ggplot() +
geom_col(aes(x= state, y= generation)) +
coord_flip()
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", source == "Solar Thermal and Photovoltaic", year != 2022) %>% group_by(year) %>%
summarize(total_generation = sum(generation)) %>% mutate(total_generation = total_generation / 1000000) %>%
ggplot() +
geom_col(aes(x= year, y= total_generation)) +
geom_smooth(aes(x = year, y= total_generation)) +
labs(title = "Total Solar Power Generation Per Year", x = "Year", y = "Power Generation(TWH)")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", source == "Wind", year != 2022) %>% group_by(year) %>%
summarize(total_generation = sum(generation)) %>% mutate(total_generation = total_generation / 1000000) %>%
ggplot() +
geom_col(aes(x= year, y= total_generation)) +
geom_smooth(aes(x = year, y= total_generation)) +
labs(title = "Total Wind Power Generation Per Year", x = "Year", y = "Power Generation(TWH)")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", source != "Total", year != 2022) %>% group_by(source, year) %>%
summarize(total_generation = sum(generation)) %>% mutate(total_generation = total_generation / 1000000) %>%
ggplot() +
geom_col(aes(x= year, y= total_generation)) +
geom_smooth(aes(x = year, y= total_generation)) +
facet_wrap(~source) +
labs(title = "Total Power Generation Per Year", x = "Year", y = "Power Generation(TWH)")
## `summarise()` has grouped output by 'source'. You can override using the
## `.groups` argument.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", source == "Wind", year != 2022) %>% group_by(year) %>%
summarize(total_generation = sum(generation)) %>% mutate(total_generation = total_generation / 1000000) %>%
ggplot() +
geom_col(aes(x= year, y= total_generation)) +
geom_smooth(aes(x = year, y= total_generation)) +
labs(title = "Total Wind Power Generation Per Year", x = "Year", y = "Power Generation(TWH)")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
energy %>% filter(producer == "Total Electric Power Industry", state != "US-TOTAL", year != 2022, source == c("Solar Thermal and Photovoltaic", "Petroleum", "Wind")) %>% group_by(Date, source) %>%
summarize(total_generation = sum(generation)) %>% mutate(total_generation = total_generation / 1000000) %>%
ggplot() +
geom_line(aes(x= Date, y= total_generation, color = source)) +
geom_smooth(aes(x = Date, y= total_generation, color = source)) +
labs(title = "Total Power Generation Over Time", x = "Date", y = "Power Generation(TWH)")
## Warning in source == c("Solar Thermal and Photovoltaic", "Petroleum", "Wind"):
## longer object length is not a multiple of shorter object length
## `summarise()` has grouped output by 'Date'. You can override using the
## `.groups` argument.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'